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It Takes Two to Tango: Two Parallel Samplers Improve Quality in Diffusion Models for Limited Steps

computer-vision › diffusion-models

📄 Abstract

Abstract: We consider the situation where we have a limited number of denoising steps, i.e., of evaluations of a diffusion model. We show that two parallel processors or samplers under such limitation can improve the quality of the sampled image. Particularly, the two samplers make denoising steps at successive times, and their information is appropriately integrated in the latent image. Remarkably, our method is simple both conceptually and to implement: it is plug-&-play, model agnostic, and does not require any additional fine-tuning or external models. We test our method with both automated and human evaluations for different diffusion models. We also show that a naive integration of the information from the two samplers lowers sample quality. Finally, we find that adding more parallel samplers does not necessarily improve sample quality.

Authors (1)

Pedro Cisneros-Velarde

Submitted

October 20, 2025

arXiv Category

cs.CV

arXiv PDF

Key Contributions

This paper proposes a novel method using two parallel samplers to improve the quality of images generated by diffusion models, especially when the number of denoising steps is limited. The key innovation lies in the simple, plug-and-play, and model-agnostic integration of information from these parallel samplers, which enhances sample quality without additional fine-tuning.

Business Value

Enables faster and higher-quality image generation from diffusion models, which can be crucial for applications requiring rapid content creation or high visual fidelity.

Paper Metadata

Innovation Type

Algorithmic Improvement

Deployment Feasibility

High, as the method is plug-and-play, model-agnostic, and requires no additional fine-tuning.

Limitations Addressed

Limited number of denoising steps in diffusion models leading to lower sample quality.

Technical Tags

diffusion modelssamplingimage generationparallel processinglatent spacedenoisingplug-and-playmodel-agnostic

Research Topics

Generative ModelsImage SynthesisSampling EfficiencyModel Adaptation

Methods & Architectures

Parallel SamplersInformation Integration Diffusion Models

Applications & Tasks

Image Generation Low-quality samples with limited stepsImproving sample quality Image SynthesisSampling

Related Fields

Computer VisionMachine LearningGenerative Models

Keywords

diffusion modelssamplingimage generationparallel samplerslimited stepsdenoisingquality improvementplug-and-playmodel-agnosticlatent image integration

Academic Context

#Generative Models#Image Synthesis#Sampling Efficiency#Model Adaptation

Commercial Potential

Potential Products

Faster image generation toolsHigh-fidelity image synthesis platforms

Target Industries

Media and EntertainmentAdvertisingGamingArt and Design

Use Case Examples

Generating high-quality images with fewer computational resourcesAccelerating creative workflows in digital art

Competitive Edge

Offers an improvement over standard diffusion sampling methods by enhancing quality under step constraints, without requiring architectural changes or retraining.

Market Opportunity

Growing market for generative AI and image synthesis tools.

Revenue Models

Integration into existing generative AI platformslicensing of the technique.

Resource Requirements

Compute Needs

Likely moderate, as it aims to improve efficiency with limited steps.

Data Requirements

None explicitly mentioned for the method itself, but requires diffusion models trained on image datasets.

Deployment Constraints

Relies on existing diffusion model implementations.

Scalability

The method is designed to work with limited steps, suggesting scalability in terms of generation speed.

Production Readiness

Maturity Level

Research

Time to Market

Short, due to plug-and-play nature.

Patent Potential

Low to Moderate, depending on the novelty of the integration technique.

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